Bayesian approach for the estimation of cyclosporine area under the curve using limited sampling strategies in pediatric hematopoietic stem cell transplantation

Abstract

Background: The optimal marker for cyclosporine (CsA) monitoring in transplantation patients remains controversial. However, there is a growing interest in the use of the area under the concentration-time curve (AUC), particularly for cyclosporine dose adjustment in pediatric hematopoietic stem cell transplantation. In this paper, we develop Bayesian limited sampling strategies (B-LSS) for cyclosporine AUC estimation using population pharmacokinetic (Pop-PK) models and investigate related issues, with the aim to improve B-LSS prediction performance.

Methods: Twenty five pediatric hematopoietic stem cell transplantation patients receiving intravenous and oral cyclosporine were investigated. Pop-PK analyses were carried out and the predictive performance of B-LSS was evaluated using the final Pop-PK model and several related ones. The performance of B-LSS when targeting different versions of AUC was also discussed.

Results: A two-compartment structure model with a lag time and a combined additive and proportional error is retained. The final covariate model does not improve the B-LSS prediction performance. The best performing models for intravenous and oral cyclosporine are the structure ones with combined and additive error, respectively. Twelve B-LSS, consisting of 4 or less sampling points obtained within 4 hours post-dose, predict AUC with 95th percentile of the absolute values of relative prediction errors of 20% or less. Moreover, B-LSS perform better for the prediction of the 'underlying' AUC derived from the Pop-PK model estimated concentrations that exclude the residual errors, in comparison to their prediction of the observed AUC directly calculated using measured concentrations.

Conclusions: B-LSS can adequately estimate cyclosporine AUC. However, B-LSS performance is not perfectly in line with the standard Pop-PK model selection criteria; hence the final model might not be ideal for AUC prediction purpose. Therefore, for B-LSS application, Pop-PK model diagnostic criteria should additionally account for AUC prediction errors.

Figure 1

Underlying AUC (7448 ng.h/ml) Vs. Observed AUC (7017 ng.h/ml).

Figure 2

Concentration-time courses for the available full profiles.

Figure 3

B-LSS development procedure; Y is the group of all profiles; Y ^(-i) is the subgroup of all profiles except the i ^th one, where i = 1,2…,N.

Figure 4

Graphical model evaluation. A: relationship between the observed and the predicted CsA concentrations based on the final parameter estimates (PRED); B: relationship between the observed and the individual predicted concentrations (IPRED); C: distribution of weighted residual (WRES) and conditional weighted residuals (CWRES).